Image Hash indexing and establishing method based on deep learning

Abstract

The invention discloses an image Hash indexing and establishing method based on deep learning and belongs to the technical field of image retrieval. The method comprises the steps that an image data set is firstly divided to obtain a test sample set, a training sample set and an image library, then the characteristic that depth characteristics extracted by a deep learning model have the very good expression capability to image semantic is fully utilized to establish different depth Hash network models of two deep convolution network structures, then each image in the test sample set and the image library undergoes forward propagations of the two models respectively, corresponding two groups of initial Hash codes are calculated, then two groups of initial Hash codes of the same image are spliced and fused to serve as fused Hash codes of the image, and similar image retrieval results are obtained by calculating and querying Hamming distances of the fused Hash codes of images and each image in the image library and arranging the Hamming distances from small to large. The image Hash indexing and establishing method makes large-scale image retrieval accurate and effective.

Description

technical field [0001] The invention relates to the technical fields of machine learning and image retrieval, in particular to a method for constructing an image hash index in image retrieval, which is expected to quickly and accurately retrieve similar images on a large-scale image data set. Background technique [0002] With the rapid development and wide application and popularization of computer, Internet and multimedia technologies, society is developing into an information society. At the same time, the image data on the Internet is growing geometrically. How to quickly and accurately find the information you want from the massive images containing rich information is the research focus of image retrieval technology. Since the early 1990s, the most commonly used image retrieval method is content-based image retrieval. This type of method has always attracted the attention of researchers, and its research hotspots mainly focus on image feature representation, similarity...

AI Technical Summary

Problems solved by technology

In order to solve the problem of feature mismatch, many researchers have proposed to use the image depth features extracted by the deep neural network as the input of the hash function to improve the retrieval effect, such as the CNNH method, but this method is a two-stage hash method, which cannot Simultaneous learning of deep features and hash codes makes the learned hash functions have certain limitations, and the proposer of this method has made improvements

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